Lab Affiliation: Elliot Botvinick Lab
Title: Deep learning-powered virtual fluorescent labeling of vascular structures in engineered tissues
Abstract: Capillary morphogenesis and angiogenesis are crucial to the fields of therapeutic implantable devices, tumor growth, and metastasis. Tissue engineering models are often used as investigational tools in these fields. Here we describe a deep learning model for imaging such tissues without the use of fluorescent labels. We examined a 3-D fibrin hydrogel co-culture system comprising vascular endothelial cells (ECs) and stromal cells (normal human dermal fibroblasts), which are required to induce spontaneous formation of capillary networks. One considerable challenge is the need to fluorescently label cells in order to identify cell type and developing structures over days or weeks. Common labeling techniques include (1) chemical fixation with fluorescence labeling that terminates each experiment timeline, (2) genetic modification to express fluorescent proteins which may alter cell phenotype, and (3) use of cell- tracking dyes that are diluted by half with each cell division. Recently, deep learning models have been used to predict labeled images from one or more unlabeled input images. Here we investigate deep learning-powered tool for predicting fluorescently labeled optical sections from a 3-D culture. Our model utilizes laser scanning reflection confocal microscopy (RCM) images at three laser wavelengths as the input and fluorescently labeled nuclei, actin cytoskeleton and vessel lumen as the ground truth. We hypothesis that our trained model will be able to predict fluorescent labels from reflection confocal images to assess cell phenotype and targeted structures during the growth of capillaries, in vitro.